IA y robótica
Definición
La IA en robótica abarca percepción (visión, tacto), planificación (movimiento, tarea) y control (actuación). Reinforcement learning and imitation learning train policies from data; sim-to-real transfer is a key challenge.
La percepción usa frecuentemente visión por computadora y a veces modelos multimodales. Las políticas de control se entrenan en simulación (DRL) or from human demonstrations; deploying to real hardware requires dealing with dynamics misigualar (sim-to-real), safety, and latency.
Cómo funciona
Sensors (cameras, force/torque, proprioception) alimentan perception models that estimate state (por ej. object poses, scene layout). Planners (classical or learned) produce trajectories or high-level actions (por ej. “pick block A”). Controllers (por ej. PID, learned policy) execute low-level commands (joint torques, velocities) to track the plan. End-to-end learning maps raw sensor input to actions in one network; modular pipelines separate perception, planning, and control for interpretability and reuse. Training is often in simulation (DRL); sim-to-real (domain randomization, system identification) and safety constraints are critical for deployment.
Casos de uso
La robótica con IA se aplica cuando la percepción, planificación o control se aprenden de datos (manipulación, navegación, sim-a-real).
- Manipulation and grasping (por ej. pick-and-place, assembly)
- Navigation and autonomous driving
- Sim-to-real and imitation learning for policy training
Documentación externa
- Spinning Up in Deep RL (OpenAI) — RL for control
- Google – Robotics — Research overview